Instructions to use zzsi/DOVE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use zzsi/DOVE with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("zzsi/DOVE", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
DOVE Model Checkpoints
This repository hosts model checkpoints for DOVE (Diffusion Priors for Video Super-Resolution).
This is not my work. All credit goes to the original authors. Please refer to the official repository for the paper, code, and license.
Contents
stage2/— Stage 2 model checkpoint (diffusion transformer, text encoder, VAE, tokenizer, scheduler)
Citation
Please cite the original paper if you use these checkpoints:
@inproceedings{zheng2025dove,
title={DOVE: Diffusion Priors for Video Super-Resolution},
author={Zheng, Chen and others},
year={2025}
}
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